Confluent - Principal Engineer

At IBM Software, we transform client challenges into solutions. Building the world's leading AI-powered, cloud-native products that shape the future of business and society. Our legacy of innovation creates endless opportunities for IBMers to learn, grow, and make an impact on a global scale. Working in Software means joining a team fueled by curiosity and collaboration. You'll work with diverse technologies, partners, and industries to design, develop, and deliver solutions that power digital transformation.

With a culture that values innovation, growth, and continuous learning, IBM Software places you at the heart of IBM's product and technology landscape. Here, you'll have the tools and opportunities to advance your career while creating software that changes the world. As a Principal Software Engineer for the Cloud Architecture and Reliability (CAR) team, you will be a technical authority on ensuring our large-scale, multi-cloud data streaming platform stays up, recovers fast, and has the right operator tools to keep data flowing.

You will work across all Confluent R&D teams to shape the architecture of the systems and our own tooling. As a lead engineer within the CAR team you will work with and through other team members to deliver on our mission. Primary Responsibilities: Resiliency: Partner with peer teams and other leaders in CAR to define the architectural patterns and standards that create systemic resiliency for Confluent Cloud. Help teams reason about how services, dependencies, and failure domains compose end to end.

Observability: Shape the architecture of a large-scale internal observability platform (metrics, logs, and traces) built on open standards such as OpenTelemetry and Prometheus. Make deliberate tradeoffs on cost, cardinality, signal attribution, and platform self-protection (quotas, rate limits, regional isolation), and make sure the platform itself is exceptionally reliable. Agentic Operator Foundations: Design and build agent-consumable operational data, tooling, and guardrails for AI-assisted triage and remediation.

Partner with Customers and Leadership: Serve as an engineering voice on reliability, resiliency, and observability requirements from regulated customers (for example financial services and public sector), and help translate regulatory and contractual expectations into engineering standards. Deep Expertise in Reliability Engineering: Proven track record of designing, operating, and improving highly available distributed systems at cloud scale, with a clear understanding of failure domains, recovery objectives (RTO/RPO), and graceful degradation.

Systems-Level Thinking: Established ability to model how many services and teams interact, find the structure underneath the complexity, and turn that into standards, guardrails, and architecture that scale beyond a single team. Mastery of Observability Fundamentals: Expertise in metrics, logs, traces, alerting, and SLOs, including the economics and failure modes of telemetry platforms at high volume and cardinality. Technical Leadership: Demonstrated ability to set direction, influence senior engineers and leaders across organizations, and drive adoption of standards you do not directly enforce.

Exceptional Written Communication: Ability to explain complex tradeoffs clearly, concisely, and with sound judgment. Hands-On Software Development: Strong coding and design skills in one or more languages with a record of delivering production tools and software. Resiliency and Recovery Design: Experience with business continuity and disaster recovery for large cloud services, including failover automation, multi-region and active-active architectures, and continuous recovery testing.

Open Source Observability Stack: Experience with Apache Druid, OpenTelemetry, Prometheus-compatible storage, Grafana, distributed tracing, etc. Regulated Environments: Familiarity with operational resilience regimes and frameworks such as DORA, FedRAMP, and SOC 2, and with translating them into engineering practice. Data Streaming and Distributed Systems: Experience with Apache Kafka, Apache Flink, or comparable platforms.

AI-Assisted Operations: Experience building or evaluating agentic tooling for operations (for example MCP-based tools, automated triage, and automated remediation), with a skeptic's eye for quality and safety. Platform Economics: Experience reducing the cost of observability or infrastructure while improving capability. United States Software Engineering Remote Professional Multiple Cities POUGHKEEPSIE LOWELL ROCHESTER TUCSON RESEARCH TRIANGLE PARK Armonk Boston Bellevue Atlanta San Jose Dallas Austin San Francisco Seattle (0147) International Business Machines Corporation Agile Methodologies Continuous Improvement Design Edge Engineering Integration Readiness Software Software Development Technical Leadership Testing Financial and Operational Combined Uniform Single Reporting Agile Coding Innovation Unit Testing Confluent Continuous Integration/Continuous Delivery Pipelines